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Cantu E, Diamond J, Ganjoo N, Nottigham A, Ramon CV, McCurry M, Friskey J, Jin D, Anderson MR, Lisowski J, Le Mahajan A, Localio AR, Gallop R, Hsu J, Christie J, Schaubel DE. Scoring donor lungs for graft failure risk: The Lung Donor Risk Index (LDRI). Am J Transplant 2024; 24:839-849. [PMID: 38266712 DOI: 10.1016/j.ajt.2024.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Lung transplantation lags behind other solid organ transplants in donor lung utilization due, in part, to uncertainty regarding donor quality. We sought to develop an easy-to-use donor risk metric that, unlike existing metrics, accounts for a rich set of donor factors. Our study population consisted of n = 26 549 adult lung transplant recipients abstracted from the United Network for Organ Sharing Standard Transplant Analysis and Research file. We used Cox regression to model graft failure (GF; earliest of death or retransplant) risk based on donor and transplant factors, adjusting for recipient factors. We then derived and validated a Lung Donor Risk Index (LDRI) and developed a pertinent online application (https://shiny.pmacs.upenn.edu/LDRI_Calculator/). We found 12 donor/transplant factors that were independently predictive of GF: age, race, insulin-dependent diabetes, the difference between donor and recipient height, smoking, cocaine use, cytomegalovirus seropositivity, creatinine, human leukocyte antigen (HLA) mismatch, ischemia time, and donation after circulatory death. Validation showed the LDRI to have GF risk discrimination that was reasonable (C = 0.61) and higher than any of its predecessors. The LDRI is intended for use by transplant centers, organ procurement organizations, and regulatory agencies and to benefit patients in decision-making. Unlike its predecessors, the proposed LDRI could gain wide acceptance because of its granularity and similarity to the Kidney Donor Risk Index.
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Affiliation(s)
- Edward Cantu
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Joshua Diamond
- Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nikhil Ganjoo
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ana Nottigham
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Christian Vivar Ramon
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Madeline McCurry
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jacqueline Friskey
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Dun Jin
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Michaela R Anderson
- Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jessica Lisowski
- Division of Cardiovascular Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Audrey Le Mahajan
- Division of Infectious Disease, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - A Russell Localio
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Gallop
- Department of Mathematics, West Chester University, West Chester, Pennsylvania, USA
| | - Jesse Hsu
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jason Christie
- Division of Pulmonary, Allergy, and Critical Care, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Douglas E Schaubel
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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Cantu E, Jin D, McCurry M, Friskey J, Lisowski J, Saleh A, Diamond JM, Anderson M, Clausen E, Hsu J, Gallop R, Christie JD, Schaubel D. Transplanting candidates with stacked risks negatively affects outcomes. J Heart Lung Transplant 2023; 42:1455-1463. [PMID: 37290569 PMCID: PMC10527778 DOI: 10.1016/j.healun.2023.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Lung transplant (LT) centers are increasingly evaluating patients with multiple risk factors for adverse outcomes. The effects of these stacked risks remains unclear. Our aim was to determine the relationship between the number of comorbidities and post-transplant outcomes. METHODS We performed a retrospective cohort study using the National Inpatient Sample (NIS) and UNOS Starfile (USF). We applied a probabilistic matching algorithm using 7 variables (transplant: month, year, and type; recipient: age, sex, race, payer). We matched recipients in the USF to transplant patients in the NIS between 2016 and 2019. The Elixhauser methodology was used to identify comorbidities present on admission. We determined the associations between mortality, length of stay (LOS), total charges, and disposition with comorbidity numbers using penalized cubic splines, Kaplan-Meier, and linear and logistic regression methods. RESULTS From 28,484,087 NIS admissions, we identified 1,821 LT recipients. Matches were exact in 76.8% of the cohort. While the remaining cohort had a probability match of ≥0.94. Penalized splines of Elixhauser comorbidity number identified 3 knots defining 3 groups of stacked risk: low (<3), medium (3-6), and high risk (>6). Inpatient mortality increased from low to medium to high-risk categories: (1.6%, 3.9%, and 7.0%; p < 0.001), as did LOS (16, 21, 29 days, p < 0.001), total charges ($553,057, $666,791, $821,641.5; p = 0.004) and discharge to a skilled nursing facility (15%, 20%, 31%; p < 0.001). CONCLUSIONS Stacked risks adversely affect post-LT mortality, LOS, charges, and discharge disposition. Further study to understand the details of specific stacked risks is warranted.
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Affiliation(s)
- Edward Cantu
- Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Dun Jin
- Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Madeline McCurry
- Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jacqueline Friskey
- Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jessica Lisowski
- Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aya Saleh
- Division of Cardiovascular Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joshua M Diamond
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michaela Anderson
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Emily Clausen
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jesse Hsu
- Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert Gallop
- Department of Mathematics, West Chester University, West Chester, Pennsylvania
| | - Jason D Christie
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Douglas Schaubel
- Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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Jin D, Mccurry M, Friskey J, Lisowski J, Diamond J, Anderson M, Crespo M, Courtwright A, Cevasco M, Bermudez C, Gallop R, Hsu Y, Christie J, Schaubel D, Cantu E. Transplanting Candidates with Stacked Risks Negatively Affects Outcomes. J Heart Lung Transplant 2023. [DOI: 10.1016/j.healun.2023.02.1619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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